Pointwise mutual information sparsely embedded feature selection

doi:10.1016/j.ijar.2022.09.012

2022-12-01

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DOI：10.1016/j.ijar.2022.09.012

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En 中文

Feature selection is an effective approach to dimensionality reduction. Feature selection based on rough sets and fuzzy rough sets is extensively realized by introducing diversified heuristic information. Most existing heuristic feature selection methods ignore the fact that different features have distinct classification abilities when constructing feature evaluation functions. Due to the limitations of search strategies, locally optimal feature subsets are probably selected out. Embedded feature selection based on regression analysis can avoid falling into locally optimal feature subsets to a certain extent. However, only linear relationships between feature space and decision space have been involved. To overcome those problems, a pointwise mutual information sparsely embedded feature selection model (PMISEFS) is proposed in this paper. In this model, the pointwise fuzzy mutual information matrix is constructed based on fuzzy information granules to characterize the discernibility of features as well as the nonlinear relationship between the feature space and decision space for a data set. A classification information matrix is introduced to further describe the consistency between the predicted decision and real decisions of samples. The fuzzy mutual information tensor is embedded sparsely into the decision matrix and the sparsely embedded coefficients, called information fusion coefficients (IFCs) of features, are adaptively learnt by imposing a smooth constraint to avoid over-fitting. An embedded feature selection algorithm is designed to adaptively learn optimal IFCs. Extensive experiments on various benchmark data sets are conducted and experimental results demonstrate the superiority of the proposed model over the state-of-the-art heuristic as well as embedded feature selection methods.(c) 2022 Elsevier Inc. All rights reserved.

Keyword:

Feature selection

Granular computing

Fuzzy mutual information

Sparse learning